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Record W4417273109 · doi:10.1016/j.agwat.2025.110066

Assessment of empirical and physically-based approaches to simulate surface resistance for improved evapotranspiration modeling of winter wheat in semi-arid region, Morocco

2025· article· en· W4417273109 on OpenAlexfundno aff
Zaineb Bouswir, Salah Er‐Raki, Jamal Ezzahar, Saïd Khabba, Abdelhakim Amazirh, Lamia Jallal, A. Chehbouni

Bibliographic record

VenueAgricultural Water Management · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
FundersUniversité Cadi AyyadUniversité Mohammed VI PolytechniqueFondation OCPMinistère de l'Enseignement Supérieur, de la Recherche Scientifique et de la Formation des CadresCentre National pour la Recherche Scientifique et TechniqueOntario College of PharmacistsInstitut de Recherche pour le Développement
KeywordsEvapotranspirationEddy covarianceIrrigationDeficit irrigationVapour Pressure DeficitCrop coefficientEmpirical modellingHydrology (agriculture)Irrigation schedulingWater use

Abstract

fetched live from OpenAlex

Evapotranspiration (ET) is a fundamental component of the water and energy balance, strongly influencing crop growth and productivity. Accurate ET estimation is critical in semi-arid regions, where water scarcity requires optimized management. Among the available approaches, the Penman–Monteith (PM) model is the most widely used for this purpose, its performance strongly depends on the accurate characterization of surface resistance ( r c ), a key parameter controlling ET estimations. In this study, two approaches for estimating r c were evaluated for winter wheat cultivated in the Haouz plain (Morocco) under contrasting irrigation regimes (full and deficit) during the 2016–2017 and 2017–2018 growing seasons. The first is a mechanistic formulation, based on the Jarvis model, which incorporates vapor pressure deficit ( VPD ) and soil water content ( θ ) to capture stomatal responses. The second is an empirical approach, using a thermal stress index ( SI ) derived from land surface temperature ( LST ), providing a rapid indicator of crop water status. Both approaches were integrated into the PM model and calibrated with eddy covariance data collected over a deficit-irrigated field in 2016/2017, then validated across both irrigation regimes and seasons. Results showed that the mechanistic approach reproduced ET dynamics under full irrigation (R² ≥ 0.73; RMSE < 0.6 mm·day⁻¹), but underestimated fluxes under severe stress. Conversely, the empirical approach, being more sensitive to short-term water status, outperformed under deficit irrigation (R² ≥ 0.79; RMSE < 0.6 mm·day⁻¹). Moreover, a critical SI threshold of 0.5 was identified, which could serve as a practical guideline for irrigation scheduling to reduce water losses. Overall, the results highlight the robustness and complementarity of both approaches and suggest the potential of hybrid models combining physiological realism with thermal sensitivity to improve irrigation management in water-limited areas. • Mechanistic and empirical approaches used to estimate canopy resistance. • The two approaches show complementarity across irrigation regimes. • Full irrigation, the mechanistic model performs best in reproducing ET (R²≥0.73). • Deficit irrigation, the empirical model performs best in reproducing ET (R²≥0.79). • SI 0.5 threshold provides a practical guideline for irrigation timing in wheat fields.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.243
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2025
Admission routes1
Has abstractyes

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